Extensions to compressed sensing theory with application to dynamic MRI

压缩感知理论的扩展及其在动态 MRI 中的应用

• 批准号: EP/F039697/1
• 负责人: Mike Davies
• 金额: $ 66.54万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FF039697%2F1
• 关键词: Extensions; compressed; sensing; theory; application

The problem of data acquisition or sampling lies at the heart of digital signal processing. It has been a long held belief that one should acquire a sufficient number of samples to satisfy the so-called Nyquist criterion. Then the discretely sampled signal is an equivalent representation of the original analogue one. However, recently, the paradigm of compressed sensing has challenged this idea. If a signal is known to have structure, and almost all signals do, then this can be used to reduce the number of samples required to define the signal; compressed sensing advocates sampling at the information rate not the Nyquist rate . This project aims to extend the existing theory of compressed sensing to include more general advanced signal models and, in particular, multi-resolution image models. These ideas should have a big impact on problems where sampling data is difficult either because it is time consuming, expensive or has associated safety issues (e.g. patient exposure to electromagnetic radiation). The project will further explore the potential of compressed sensing as a novel compression strategy for possible use in distributed or remote sensing applications. The project will use these ideas to develop new rapid Magnetic Resonance Imaging (MRI) acquisition systems. The advantages of accelerated scan times are manifold. It enables clinicians to take higher resolution scans and to acquire more detailed dynamic image sequences (e.g. for cardiac diagnosis). Furthermore, with the trend to the increased use of high field scanners reducing the samples for a given image acquisition has the additional benefit of lowering the RF exposure that the patient is subjected to.

数据采集或采样问题是数字信号处理的核心问题。长期以来，人们一直认为，一个人应该获得足够数量的样本，以满足所谓的奈奎斯特标准。则离散采样的信号是原始模拟信号的等价表示。然而，最近，压缩传感的范例对这一想法提出了挑战。如果已知信号具有结构，并且几乎所有信号都具有结构，则可以利用这一点来减少定义信号所需的样本数量；压缩传感提倡以信息速率而不是奈奎斯特速率进行采样。这个项目旨在扩展现有的压缩感知理论，以包括更一般的高级信号模型，特别是多分辨率图像模型。这些想法应该会对采样数据困难的问题产生重大影响，因为采样数据既耗时、昂贵，又有相关的安全问题(例如患者暴露在电磁辐射中)。该项目将进一步探讨压缩传感作为一种可能用于分布式或遥感应用的新的压缩战略的潜力。该项目将利用这些想法开发新的快速磁共振成像(MRI)采集系统。加速扫描时间的优势是多方面的。它使临床医生能够进行更高分辨率的扫描，并获得更详细的动态图像序列(例如，用于心脏诊断)。此外，随着越来越多地使用高场扫描仪，减少特定图像采集的样本具有降低患者遭受的射频暴露的额外好处。

项目成果

Greedy-like algorithms for the cosparse analysis model

• DOI: 10.1016/j.laa.2013.03.004
• 发表时间: 2012-07
• 期刊: Linear Algebra and its Applications
• 影响因子: 1.1
• 作者: R. Giryes;Sangnam Nam;Michael Elad;R. Gribonval;M. Davies

Detection and segmentation of fmcw radar signals based on the chirplet transform

• DOI: 10.1109/icassp.2011.5946844
• 发表时间: 2011-05
• 期刊: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: F. Millioz;M. Davies

Iterative hard thresholding for compressed sensing

• DOI: 10.1016/j.acha.2009.04.002
• 发表时间: 2009-11-01
• 期刊: APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS
• 影响因子: 2.5
• 作者: Blumensath, Thomas;Davies, Mike E.
• 通讯作者: Davies, Mike E.

Radiological and quantitative assessment of Compressed Sensing reconstruction of undersampled 3D brain images

欠采样 3D 脑图像压缩感知重建的放射学和定量评估

• 发表时间: 2015
• 作者: Ian Marshall

Sample-distortion functions for compressed sensing

• DOI: 10.1109/allerton.2011.6120262
• 发表时间: 2011-09
• 期刊: 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
• 作者: M. Davies;Chunli Guo